Case Study of Rostherne Mere and

Some Aspects of Indirect Interactions in Aquatic Ecosystems:

Case Study of Rostherne Mere and

Implications for Environmental Management

& Comparative Theoretical Ecosystem Analysis

Vladimir Krivtsov

Examples of Important Indirect Relationships

)

Mesozoic volcanic activity – extinction of dinosaurs

)

Increased use of fertilisers – eutrophication and deterioration of water quality

)

Increased consumption of fossil fuels – greenhouse effect, global warming and increased frequency of natural disasters

)

Increased use of CFC – depletion of ozone layer

)

Activity of proterozoic cyanobacteria –

development of modern atmosphere & Homo

Sapiens

Approaches to Study Indirect Relationships

)

Theoretical

)

Experimental

)

Observational

---

The above are combined in the Comparative Theoretical Ecosystem Analysis (CTEA)

(Ecol. Modelling 174, 37-54)

Case Study of Coupled Biogeochemical

Cycles in Rostherne Mere

Chlorophyll_a, microgram/l (data for 1996 and model simulation)

0 20 40 60 80 100 120 140 160

1 20 39 58 77 96 115 134 153 172 191 210 229 248 267 286 305 324 343 362 Julian Day

Observed Simulated

DIATOMS

(S TEP HANODIS CUS , AS TERIONELLA)

ANABAENA

MIXED

P HYTOP LANKTON P EAK

DIATOMS

BIOLATE = 7.6E7 –162617* STBIG

BIOLATE was calculated as sum of the maximum plankton counts (Anabaena, Aphanizomenon, Ceratium, Microcystis) multiplied by characteristic biovolume values

(found in Reynolds & Bellinger, 1992).

BIOLATE

STBIG

400 300

200 100

0 -100

100000000

80000000

60000000

40000000

20000000

0

Observed Linear

Changes in Temperature Profile

0 m 13 m

25 m

30.05.96 4.07.96 18.07.96 8.08.96 21.08.96 13.09 9.10.96 5.11.96 3.12.96

0 5 10 15 20

25

T

DEPTH

0 0.5 1 1.5 2 2.5 3 3.5 4

27- Feb5-Mar

12- Ma

r 19-

Ma r 26-

Mar2-Apr 9-Apr

16- Apr

23- Apr

30- Apr7-May

14- May

21- May

28-May4-Jun 11-Jun

18- Jun

25-Jun 2-Jul 9-Jul16- Jul

23- Jul

30- Jul

6-Aug 13-

Aug 20-

Aug 27-

Aug3-Sep 10-

Sep 17-

Sep 24-

Sep1-Oct 8-Oct

15- Oct

22- Oct

29- Oct

5-Nov 12-

Nov 19-

Nov 26-

Nov3-Dec

NH4 NOx Organic N

Changes in dissolved N species (ppm)

Changes in dissolved P species (ppm)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

27-Feb 5-Ma r

12-Mar 19-Mar

26-Mar 2-Apr 9-Apr

16-Apr 23-Apr

30- Apr

7-Ma y

14- May

21- May

28-May 4-Jun 11-

Jun 18-

Jun

25-Jun 2-Jul 9-J

ul 16-

Jul 23-

Jul 30-

Jul 6-A

ug 13-

Aug 20-

Aug 27-

Aug 3-S

ep 10-

Sep 17-

Sep 24-

Sep 1-O

ct 8-O

ct 15-

Oct 22-

Oct 29-

Oct5-Nov

PO4 Organic P

Changes in Si Profile

3

1016

22 26.04.96

18.07.96

8.08.96

21.08.96

13.09.96

9.10.96

5.11.96

3.12.96

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Si (p p m )

De p th (m )

3

10 16

22

18.07.96

8.08.96

21.08.96

13.09.96

9.10.96

5.11.96

3.12.96

0 0.2 0.4 0.6 0.8

1

PO4 (ppm)

Depth (m)

Increased concentrations of phosphates

) eutrophication of waterbodies

) unsightly cyanobacterial blooms

) depreciation of the amenity value

) decrease of the water quality

) poisoning of people and livestock

MODEL 'ROSTHERNE'

(Hydrological Processes 14, 1489-1506, Ecol. Modelling 113, 95-123)

• CATCHMENT SUBMODEL

• EVAPORATION & EVAPOTRANSPIRATION

• SURFACE RUNOFF

• INFLOW AND OUTFLOW

• STRATIFICATION

• BIOGEOCHEMICAL CYCLES OF P, SI, N.

• INTERDEPENDENCIES OF NUTRIENT UPTAKE

• VARIABLE INTERNAL STORES

• TEMPERATURE AND LIGHT LIMITATION

• LOSS: MORTALITY, SEDIMENTATION, WASHOUT

)

d Phyto(j)/dt = Phyto(j) * (Growth(j) - Mortality(j) - SedimentRate(j) / D - 1 /

FlushRate),

Growth(j) = Light_Lim * MaxGrowth(j) *

*QOLim(LimitingChemical, j)

---

)

d Q(i,j)/dt= CellUptake(i, j) - Excretion(i, j) -

Growth(j),

CellUptake(i, j) = BigestUptake(i, j) * eLim(i, j) * QmLim(i, j) *

Tlim*QOLim(LimitingChemical,j) ^ Correction

)

d DeadPhyto(j)/dt=Mortality(j) * Phyto(j) - (Smax(j) / D + DECOMP + 1 / FlushRate) * DeadPhyto(j)

)

d DetritusChemicalPhyto(i, j)= Mortality(j) * Phyto(j) * Q(i,j) - DECOMP *

DetritusChemicalPhyto(i, j) * TLim - (Smax(j) / D + 1 / FlushRate) * DetritusChemicalPhyto(i,j)

)

d Conc(i)/dt= (InConc(i) - Conc(i)) / FlushRate

– ∑∑(Phyto(j) * CellUptake(i, j) - DECOMP *

DetritusChemicalPhyto(i, j) * Tlim)

Chlorophyll_a, microgram/l (data for 1996 and model simulation)

0 20 40 60 80 100 120 140 160

1 20 39 58 77 96 115 134 153 172 191 210 229 248 267 286 305 324 343 362 Julian Day

Observed Simulated

DIATOMS

(S TEP HANODIS CUS , AS TERIONELLA)

ANABAENA

MIXED

P HYTOP LANKTON P EAK

DIATOMS

DIATOMS

BIOGEOCHEMICAL MANIPULATION - CONCEPT DEVELOPMENT

phytoplankton community in temperate lakes

In Summer

) dominated by cyanobacteria

) limited by P

)

in Spring

) dominated by diatoms

) limited by Si

Increase of available Si in Spring

) increased diatom growth

) increase of P uptake by Diatoms

) increased P removal from the epilimnion

) decrease of a nuisance Summer

bloom

IMPLEMENTATION REQUIRE:

)

Prediction of timing and other parameters of the bloom

)

Calculation of the necessary amount of Si and means of its delivery (e.g. additions to inflows, spreading over the terrestrial

catchment, increasing weathering)

)

Detailed account of terrestrial processes in the watershed

---

i.e. SPECIAL SIMULATION MODEL

IS INDISPENSABLE

MICROS

CHLSPR

50 40

30 20

10 0

1200

1000

800

600

400

200

0

Observed Linear

Relationship between maximum spring chlorophyll levels (CHLSPR, ppb) and late summer maximum of Microcystis counts (MICROS, colonies/ml). Straight regression line

(r2 = 0.64, p=0.018) is described by the following equation:

MICROS= 1065.22 –21.915* CHLSPR

If in the model Diatoms are limited by Zooplankton, then decrease of Zooplankton in Spring results in the decrease of Cyanobacterial Dominance in Summer

If Diatoms are limited by Stratification, then earlier

establishment of stratification will result in the decrease of Cyanobacterial Dominance in Summer

(Ecol. Modelling 133, 73-82)

However, complex interaction between temperature, zooplankton and fish may offset the interdependence between the spring and the summer blooms!

(Ecol. Modelling 138, 153-171)

Indirect Regulation Rule for Consecutive Stages of Ecological Succession

If Component A is dominant at Stage 1 and limited by X And Component B is dominant at Stage 2 and limited by Y Then, provided functioning of A also results in the decrease

(e.g. Consumption) of Y, and the regeneration of Y is sufficiently slow,

Any process alleviating the limitation of A at Stage 1 will inevitably result in the decrease of the Dominance of B at Stage 2

(Russian Journal of Ecology 32, 230-234)

CONCLUSIONS

)

In lakes and their terrestrial catchments P

biogeochemical cycle could be regulated by Si cycle

)

Magnitude of the Summer cyanobacterial

bloom could be regulated by alteration of the spring diatom bloom, and, therefore, by Si

supply from terrestrial catchment

)

For the above a special simulation model

such as “ROSTHERNE” is indispensable

Conclusions

Because the flushing rate of the lake is low and the estimated internal

loading is relatively high, rapid

changes in the trophic status of the ecosystem without chemical and/or biological measures seem very

unlikely.

Conclusions

) Indirect interrelations between ecosystem processes are often realised after a considerable time lag and/or separated spatially, and are not obvious

) The understanding of these relationships is

indispensable for sustainable development and ecomanagement

) An emerging framework of CTEA calls for the

observational, theoretical and experimental methods to be applied in concert

) This will allow elucidation of the role of indirect

effects in ecosystem succession, evolution, recovery after pollution, disturbance, and alterations of

prevalent patterns due to changes in management practices.